Integrated Protein Solubility Shift Assays for Comprehensive Drug Target Identification on a Proteome-Wide Scale.

Target proteins are often stabilized after binding with a ligand and thereby typically become more resistant to denaturation. Based on this phenomenon, several methods without the need to covalently modify the ligand have been developed to identify target proteins for a specific ligand. These methods usually employ complicated workflows with high cost and limited throughput. Here, we develop an iso-pH shift assay (ipHSA) method, a proteome-wide target identification method that detects ligand-induced protein solubility shifts by precipitating proteins with a single concentration of acidic agent followed by protein quantification via data-independent acquisition (DIA). Using a pan-kinase inhibitor, staurosporine, we demonstrated that ipHSA increased throughput compared to the previously developed pH-dependent protein precipitation (pHDPP) method. ipHSA was found to have high complementarity in staurosporine target identification compared with the improved isothermal shift assay (iTSA) and isosolvent shift assay (iSSA) using DIA instead of tandem mass tags (TMTs) for quantification. To further improve target identification sensitivity, we developed an integrated protein solubility shift assay (IPSSA) by pooling the supernatants yielded from ipHSA, iTSA, and iSSA methods. IPSSA exhibited increased sensitivity in screening staurosporine targets by 38, 29, and 38% compared to individual methods. Increasing the number of replicate experiments further enhanced the sensitivity of target identification. Meanwhile, IPSSA also improved the throughput and reduced the cost compared with previous methods. As a fast and efficient tool for drug target identification, IPSSA is expected to have broad applications in the study of the mechanism of action.

Highly effective identification of drug targets at the proteome level by pH-dependent protein precipitation.

Fully understanding the target spaces of drugs is essential for investigating the mechanism of drug action and side effects, as well as for drug discovery and repurposing. In this study, we present an energetics-based approach, termed pH-dependent protein precipitation (pHDPP), to probe the ligand-induced protein stability shift for proteome-wide drug target identification. We demonstrate that pHDPP works for a diverse array of ligands, including a folate derivative, an ATP analog, a CDK inhibitor and an immunosuppressant, enabling highly specific identification of target proteins from total cell lysates. This approach is compared to thermal and solvent-induced denaturation approaches with a pan-kinase inhibitor as the model drug, demonstrating its high sensitivity and high complementarity to other approaches. Dihydroartemisinin (DHA), a dominant derivative of artemisinin to treat malaria, is known to have an extraordinary effect on the treatment of various cancers. However, the anti-tumor mechanisms remain unknown. pHDPP was applied to reveal the target space of DHA and 45 potential target proteins were identified. Pathway analysis indicated that these target proteins were mainly involved in metabolism and apoptosis pathways. Two cancer-related target proteins, ALDH7A1 and HMGB1, were validated by structural simulation and AI-based target prediction methods. And they were further validated to have strong affinity to DHA by using cellular thermal shift assay (CETSA). In summary, pHDPP is a powerful tool to construct the target protein space to reveal the mechanism of drug action and would have broad application in drug discovery studies.

Matrix Thermal Shift Assay for Fast Construction of Multidimensional Ligand-Target Space.

Existing thermal shift-based mass spectrometry approaches are able to identify target proteins without chemical modification of the ligand, but they are suffering from complicated workflows with limited throughput. Herein, we present a new thermal shift-based method, termed matrix thermal shift assay (mTSA), for fast deconvolution of ligand-binding targets and binding affinities at the proteome level. In mTSA, a sample matrix, treated horizontally with five different compound concentrations and vertically with five technical replicates of each condition, was denatured at a single temperature to induce protein precipitation, and then, data-independent acquisition was employed for quick protein quantification. Compared with previous thermal shift assays, the analysis throughput of mTSA was significantly improved, but the costs as well as efforts were reduced. More importantly, the matrix experiment design allowed simultaneous computation of the statistical significance and fitting of the dose-response profiles, which can be combined to enable a more accurate identification of target proteins, as well as reporting binding affinities between the ligand and individual targets. Using a pan-specific kinase inhibitor, staurosporine, we demonstrated a 36% improvement in screening sensitivity over the traditional thermal proteome profiling (TPP) and a comparable sensitivity with a latest two-dimensional TPP. Finally, mTSA was successfully applied to delineate the target landscape of perfluorooctanesulfonic acid (PFOS), a persistent organic pollutant that is hard to perform modification on, and revealed several potential targets that might account for the toxicities of PFOS.

Proteome-Wide Deconvolution of Drug Targets and Binding Sites by Lysine Reactivity Profiling.

Recently, numerous efforts have been devoted to identifying drug targets and binding sites in complex proteomes, which is of great importance in modern drug discovery. In this study, we developed a robust lysine reactivity profiling method to systematically study drug-binding targets and binding sites at the proteome level. This method is based on the principle that binding of a drug to a specific region of target proteins will change the reactivity of lysine residues that are located at this region, and these changes can be detected with an enrichable and lysine reactive probe. Coupled with data-independent acquisition (DIA), the known target proteins and corresponding binding sites were successfully revealed from K562 cell lysates for three model drugs: geldanamycin, staurosporine, and dasatinib. In addition, the drug-induced conformational changes of certain targets were also revealed by our method during the screening of staurosporine. The screening sensitivity of our method revealed from the screening of stuarosporine and dasatinib was comparable with that of thermal proteome profiling (TPP) or machine learning-based limited proteolysis (LiP-Quant). Overall, 21 and 4 kinase targets, including adenosine 5'-triphosphate (ATP)-binding targets, were identified for staurosporine and dasatinib in K562 cell lysates, respectively. We found that target proteins identified by TPP, LiP-Quant, and our method were complementary, emphasizing that the development of new methods that probe different properties of proteins is of great importance in drug target deconvolution. We also envision further applications of our method in proteome-wide probing multiple events that involve lysine reactivity changes.

Precipitate-Supported Thermal Proteome Profiling Coupled with Deep Learning for Comprehensive Screening of Drug Target Proteins.

Although thermal proteome profiling (TPP) acts as a popular modification-free approach for drug target deconvolution, some key problems are still limiting screening sensitivity. In the prevailing TPP workflow, only the soluble fractions are analyzed after thermal treatment, while the precipitate fractions that also contain abundant information of drug-induced stability shifts are discarded; the sigmoid melting curve fitting strategy used for data processing suffers from discriminations for a part of human proteome with multiple transitions. In this study, a precipitate-supported TPP (PSTPP) assay was presented for unbiased and comprehensive analysis of protein-drug interactions at the proteome level. In PSTPP, only these temperatures where significant precipitation is observed were applied to induce protein denaturation and the complementary information contained in both supernatant fractions and precipitate fractions was used to improve the screening specificity and sensitivity. In addition, a novel image recognition algorithm based on deep learning was developed to recognize the target proteins, which circumvented the problems that exist in the sigmoid curve fitting strategy. PSTPP assay was validated by identifying the known targets of methotrexate, raltitrexed, and SNS-032 with good performance. Using a promiscuous kinase inhibitor, staurosporine, we delineated 99 kinase targets with a specificity up to 83% in K562 cell lysates, which represented a significant improvement over the existing thermal shift methods. Furthermore, the PSTPP strategy was successfully applied to analyze the binding targets of rapamycin, identifying the well-known targets, FKBP1A, as well as revealing a few other potential targets.

Mechanical stress induced protein precipitation method for drug target screening.

The process of protein precipitation can be used to decipher the interaction of ligand and protein. For example, the classic Thermal Proteome Profiling (TPP) method uses heating as the driving force for protein precipitation, to discover the drug target protein. Under heating or other denature forces, the target protein that binds with the drug compound will be more resistant to precipitation than the free protein. Similar to thermal stress, mechanical stress can also induce protein precipitation. Upon mechanical stress, protein will gradually precipitate along with protein conformational changes, which can be exploited for the study of the ligand-protein interaction. Herein, we proposed a Mechanical Stress Induced Protein Precipitation (MSIPP) method for drug target deconvolution. Its streamlined workflow allows in situ sample preparation on the surface of microparticles, from protein precipitation to digestion. The mechanical stress was generated by vortexing the slurry of protein solution and microparticle materials. The mechanical stress induced protein precipitate was captured by the microparticles, which guarantees the MSIPP method to be scalable and user-friendly. The MSIPP method was successfully applied to four drug compounds, Methotrexate, Raltitrexed, SHP099, Geldanamycin and a pan-inhibitor of protein kinases, Staurosporine. Besides, DHFR was demonstrated to be a target of Raltitrexed, which has not been revealed by any other modification-free drug target discovery method yet. Thus, MSIPP is a complementary method to other drug target screening methods.

Microparticle-Assisted Precipitation Screening Method for Robust Drug Target Identification.

While thermal proteome profiling (TPP) shines in the field of drug target screening by analyzing the soluble fraction of the proteome samples treated at high temperature, the counterpart, the insoluble precipitate, has been overlooked for a long time. The analysis of the precipitate is hampered by the inefficient sample processing procedure. Herein, we propose a novel method, termed microparticle-assisted precipitation screening (MAPS), for drug target identification. The MAPS method exploits the principle that drug-bound proteins will be more resistant to thermal unfolding similar to the classic TPP method, but the process of protein precipitation is assisted by microparticles. Upon heating, proteins unfold and aggregate on the surface of the microparticles. The introduction of a microparticle simplifies the whole sample preparation workflow. The proteins that precipitate on the microparticles are subjected to washing, alkylation, and digestion. The whole sample preparation is processed conveniently on the surface of the microparticles without any transfer. With the assistance of microparticles, sample loss is minimized. The MAPS method is compatible with minute amounts of initial proteins. MAPS was applied to screen the targets of several well-studied drugs and the known target proteins were successfully identified with high confidence and specificity. To investigate the specificity of the method, MAPS was applied to screen the targets of the pan-kinase inhibitor, staurosporine, and 32 protein kinases (specificity of 80%) were identified using only 20 µg of initial proteins of each sample. MAPS is an unbiased robust method for drug target screening, filling the vacancy of stability-based target screening using a precipitate.

A Simplified Thermal Proteome Profiling Approach to Screen Protein Targets of a Ligand.

Thermal proteome profiling is a powerful energetic-based chemical proteomics method to reveal the ligand-protein interaction. However, the costly multiplexed isotopic labeling reagent, mainly Multiplexed isobaric tandem mass tag (TMT), and the long mass spectrometric time limits the wide application of this method. Here a simple and cost-effective strategy by using dimethyl labeling technique instead of TMT labeling is reported to quantify proteins and by using the peptides derived from the same protein to determine significantly changed proteins in one LC-MS run. This method is validated by identifying the known targets of methotrexate and geldanamycin. In addition, several potential off-targets involved in detoxification of reactive oxygen species pathway are also discovered for geldanamycin. This method is further applied to map the interactome of adenosine triphosphate (ATP) in the 293T cell lysate by using ATP analogue, adenylyl imidodiphosphate (AMP-PNP), as the ligand. As a result, a total of 123 AMP-PNP-sensitive proteins are found, of which 59 proteins are stabilized by AMP-PNP. Approximately 53% and 20% of these stabilized candidate protein targets are known as ATP and RNA binding proteins. Overall, above results demonstrated that this approach could be a valuable platform for the unbiased target proteins identification with reduced reagent cost and mass spectrometric time.

Solvent-Induced Protein Precipitation for Drug Target Discovery on the Proteomic Scale.

High-throughput drug discovery is highly dependent on the targets available to accelerate the process of candidates screening. Traditional chemical proteomics approaches for the screening of drug targets usually require the immobilization/modification of the drug molecules to pull down the interacting proteins. Recently, energetics-based proteomics methods provide an alternative way to study drug-protein interaction by using complex cell lysate directly without any modification of the drugs. In this study, we developed a novel energetics-based proteomics strategy, the solvent-induced protein precipitation (SIP) approach, to profile the interaction of drugs with their target proteins by using quantitative proteomics. The method is easy to use for any laboratory with the common chemical reagents of acetone, ethanol, and acetic acid. The SIP approach was able to identify the well-known protein targets of methotrexate, SNS-032, and a pan-kinase inhibitor of staurosporine in cell lysate. We further applied this approach to discover the off-targets of geldanamycin. Three known protein targets of the HSP90 family were successfully identified, and several potential off-targets including NADH dehydrogenase subunits NDUFV1 and NDUFAB1 were identified for the first time, and the NDUFV1 was validated by using Western blotting. In addition, this approach was capable of evaluating the affinity of the drug-target interaction. The data collectively proved that our approach provides a powerful platform for drug target discovery.